no code implementations • 31 Mar 2024 • Haoxuan Qu, Yujun Cai, Jun Liu
Motivated by this, we propose a novel LLM-AR framework, in which we investigate treating the Large Language Model as an Action Recognizer.
no code implementations • 22 Mar 2024 • Haoxuan Qu, Ziyan Guo, Jun Liu
Recently, while text-driven human motion generation has received massive research attention, most existing text-driven motion generators are generally only designed to generate motion sequences in a blank background.
no code implementations • 15 Mar 2024 • Hang Zhang, Wenxiao Zhang, Haoxuan Qu, Jun Liu
Human-centered dynamic scene understanding plays a pivotal role in enhancing the capability of robotic and autonomous systems, in which Video-based Human-Object Interaction (V-HOI) detection is a crucial task in semantic scene understanding, aimed at comprehensively understanding HOI relationships within a video to benefit the behavioral decisions of mobile robots and autonomous driving systems.
no code implementations • 29 Dec 2023 • Li Xu, Haoxuan Qu, Yujun Cai, Jun Liu
Estimating the 6D object pose from a single RGB image often involves noise and indeterminacy due to challenges such as occlusions and cluttered backgrounds.
1 code implementation • NeurIPS 2023 • Haoxuan Qu, Xiaofei Hui, Yujun Cai, Jun Liu
Open-set object recognition aims to identify if an object is from a class that has been encountered during training or not.
no code implementations • CVPR 2023 • Haoxuan Qu, Yujun Cai, Lin Geng Foo, Ajay Kumar, Jun Liu
Therefore, via minimizing the distance between the two characteristic functions, we can optimize the model to provide a more accurate localization result for the body joints in different sub-regions of the predicted heatmap.
no code implementations • 13 Oct 2022 • Haoxuan Qu, Yanchao Li, Lin Geng Foo, Jason Kuen, Jiuxiang Gu, Jun Liu
Confidence estimation, a task that aims to evaluate the trustworthiness of the model's prediction output during deployment, has received lots of research attention recently, due to its importance for the safe deployment of deep models.
no code implementations • 3 Oct 2022 • Haoxuan Qu, Li Xu, Yujun Cai, Lin Geng Foo, Jun Liu
In this paper, we show that optimizing the heatmap prediction in such a way, the model performance of body joint localization, which is the intrinsic objective of this task, may not be consistently improved during the optimization process of the heatmap prediction.
no code implementations • 23 Jul 2022 • Li Xu, Haoxuan Qu, Jason Kuen, Jiuxiang Gu, Jun Liu
Video scene graph generation (VidSGG) aims to parse the video content into scene graphs, which involves modeling the spatio-temporal contextual information in the video.
no code implementations • 23 Sep 2021 • Haoxuan Qu, Hossein Rahmani, Li Xu, Bryan Williams, Jun Liu
In contrast to batch learning where all training data is available at once, continual learning represents a family of methods that accumulate knowledge and learn continuously with data available in sequential order.